Search Results - (( data generation based algorithm ) OR ( data classification problems algorithm ))
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1
Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…This project focused on three main objectives: to investigate dengue data and Clonal Selection Algorithm for classification of Dengue, to design and develops Clonal Selection Classification System (CSCS) and to evaluate Clonal Selection Classification System symptoms. …”
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2
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
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3
Mutable composite firefly algorithm for gene selection in microarray based cancer classification
Published 2022“…A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. …”
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4
An enhancement of classification technique based on rough set theory for intrusion detection system application
Published 2019“…Thus, to deal with huge dataset, data mining technique can be improved by introducing discretization algorithm to increase classification performance. …”
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5
The Bacterial Foraging Optimisation Algorithm using Prototype Selection and Prototype Generation for Data Classification
Published 2020“…Thus, this study aims to adopt and modify the BFOA into Instance Selection (IS) classifier by manipulating its global search capability and high convergence rate for data classification problem. There are two modified instance-based classifiers of BFOA were developed in this study. …”
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6
A new ant based rule extraction algorithm for web classification
Published 2011“…Web documents contain enormous number of attributes as compared to other type of data. Ant-Miner algorithm is also still lacking in efficiency, accuracy and rule simplicity because of the local minima problem.Therefore, the Ant-Miner algorithm needs to be improved by taking into consideration of the accuracy and rule simplicity criteria so that it could be used to classify Web documents data sets or any large data sets.The best attribute selection method for Web texts categorization is the combination of correlation-based evaluation with random search as the search method.However, this attribute selection method will not give the best performance in attributes reduction. …”
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Propositional satisfiability algorithm to find minimal reducts for data mining
Published 2002“…An algorithm in finding minimal reducts based on Prepositional Satisfiability (SAT) algorithm is proposed. …”
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An improved particle swarm optimization algorithm for data classification
Published 2023“…Optimisation-based methods are enormously used in the field of data classification. …”
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Evaluation and optimization of frequent association rule based classification
Published 2014“…Works on sustaining the interestingness of rules generated by data mining algorithms are actively and constantly being examined and developed. …”
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Ideal combination feature selection model for classification problem based on bio-inspired approach
Published 2020“…The aim of this paper is to exploit the capability of bio-inspired search algorithms, together with wrapper and filtered methods in generating optimal set of features. …”
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The components of the AIRS2 algorithm that pose problems will be modified. This thesis proposes three new hybrid algorithms: The FRA-AIRS2 algorithm uses fuzzy logic to improve data reduction capability of AIRS2 and to solve the linearity problem associated with resource allocation of AIRS. …”
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12
Utilisation of Exponential-Based Resource Allocation and Competition in Artificial Immune Recognition System
Published 2011“…Artificial Immune Recognition System is one of the several immune inspired algorithms that can be used to perform classification, a data mining task. …”
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13
IP algorithms in compact rough classification modeling
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Hybrid ant colony optimization and genetic algorithm for rule induction
Published 2020“…In our proposed hybrid ACO/GA algorithm, the ACO is responsible for generating classification rules and the GA improves the classification rules iteratively using the principles of multi-neighborhood structure (i.e., mutation and crossover) procedures to overcome the local optima problem. …”
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15
Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
Published 2009“…Therefore for that reason we develop another algorithm for clustering space which contributes a higher accuracy compares to K-means cluster with less subclass number, higher stability and bounded time of classification with respect to the variable data size. …”
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Sentiment analysis of restaurant reviews in Kuala Terengganu based on K-Nearest Neighbor/ Siti Syazwana Jafri
Published 2021“…In the future, it is very recommended to experiment with different algorithm. The volume of data should be large as it can generate better result of classification method.…”
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17
Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Based on the above components and circumstances, many studies have been performed on data clustering problems. …”
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18
Deep learning-based colorectal cancer classification using augmented and normalised gut microbiome data / Mwenge Mulenga
Published 2022“…Second, to develop novel techniques that combine the strengths of normalisation, feature engineering and data augmentation to address the problem of dimensionality, feature dominance and sparsity in colorectal cancer identification based on gut microbiome data, using deep neural network algorithms. …”
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Non-fiducial based electrocardiogram biometrics with kernel methods
Published 2017“…The effectiveness of this algorithm is investigated by comparing with other AC based feature extraction algorithms involving AC/LDA (Linear Discriminant Analysis) and AC/PCA (Principal Component Analysis). …”
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An improved method using fuzzy system based on hybrid boahs for phishing attack detection
Published 2022“…However, it is difficult to generate fuzzy parameters manually to classify data when it comes to a very complex problem. …”
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